Comparative analysis of field ration for military personnel of the ukrainian army and armies of other countries worldwide
Bibliographic record
Abstract
For the purpose of improvement of the Ukrainian nutritional standards this Article provides comparative analysis of field rations of different countries worldwide to make a proposal on improvement of food-stuff assortment in food ration for military personnel in the Armed Forces of Ukraine, Army of USA, the British Army, Army of Germany, Army of Italy, Army of Canada, Army of France, Army of Belarus, Army of Armenia. In accordance with the comparative analysis it was established that ration composition used for the Armed Forces of Ukraine military personnel lags behind developed countries of the world both in nutrition arrangement and in nutrient composition, especially in relation to assortment and variety of ration food-stuff. Moreover, a field ration is strictly unified and doesn’t consider individual needs of military personnel in calories, proteins, fats, carbohydrates, food fibers. Selection of individual field ration takes to account only age of military personnel, i. e. individual needs related to nutrition composition such as physical abilities, level of physical activity, gender, type of occupation before military conscription and etc. are not consideredThe obtained results confirms practicability of assortment products assortment included to field rations for the purpose to correct nutrition rations towards optimal balance for military efficiency of army, adaptation of military personnel to physical and psychological loads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".